arXiv subjects

Hariswaran Sitaraman

Publications and source records attributed to Hariswaran Sitaraman.

8 recordsLinked to original sources

arXiv2026

Spatiotemporal Dynamics of Hydrogen Plasma Smelting Reduction of iron ore: A Multi-Species Diagnostic Approach

Plasma-based mineral-processing routes, such as hydrogen plasma smelting reduction (HPSR), which converts iron-ore fines directly to liquid metal in a single scalable step are commonly modeled by treating the arc as a spatially uniform heat source. Yet the reduction chemistry is governed by the strongly non-uniform conditions at the plasma-melt interface, which spatially averaged diagnostics cannot resolve. Here we spatially and temporally resolve the arc of a transferred arc HPSR reactor using multi-species optical emission spectroscopy (OES), in which neutral and ionic argon (Ar I, Ar II), hydrogen Balmer, and neutral iron (Fe I) emissions serve as intrinsic spatial filters set by their differing ionization thresholds. Combined with infrared thermography of the melt surface and an LTE thermal-plasma model validated against the benchmark free-burning argon arc, the measurements reveal a strongly stratified, non-isothermal discharge: an argon-defined core (>10,000 K), a partially recombined Balmer envelope (7,000-10,000 K), and an Fe I-traced interfacial boundary layer (3,000-4,000 K) directly above a melt surface at ~1,900-2,300 K. Across this steep thermal drop, positive hydrogen ions recombine before reaching the surface, so the reductant flux delivered to the oxide is overwhelmingly neutral; atomic hydrogen (H) and vibrationally excited molecular hydrogen H2(v), rather than the energetic ions often assumed. The measured electron density and excitation temperature bound the interfacial ionization. These findings redefine the boundary conditions for kinetic modeling of plasma-based ore reduction and establish a spatially resolved multi-species diagnostic framework transferable across plasma mineral-processing systems.

physics.plasm-ph↗

arXiv2025

Vidyut3d: a GPU accelerated fluid solver for non-equilibrium plasmas on adaptive grids

We present the numerical methods, programming methodology, verification, and performance assessment of a non-equilibrium plasma fluid solver that can effectively utilize current and upcoming central processing and graphics processing unit (CPU+GPU) architectures, in this work. Our plasma fluid model solves the coupled conservation equations for species transport, electrostatic Poisson and electron temperature on adaptive Cartesian grids. Our solver is written using performance portable adaptive-grid/particle management library, AMReX, and is portable over widely available vendor specific GPU architectures. We present verification of our solver using method of manufactured solutions that indicate formal second order accuracy with central diffusion and fifth-order weighted-essentially-non-oscillatory (WENO) advection scheme. We also verify our solver with published literature on capacitive discharges and atmospheric pressure streamer propagation. We demonstrate the use of our solver on two 3D simulation cases: an atmospheric streamer propagation in Ar-H2 mixtures and a low pressure twin electrode radio frequency reactor. Our performance studies on three different CPU+GPU architectures indicate approximately 150-400X speed-up using AMD and NVIDIA GPUs per time step compared to a single CPU core for a 4 million cell simulation with 15 species.

physics.plasm-ph↗

arXiv2025

Adaptive Computing for Scale-up Problems

Adaptive Computing is an application-agnostic outer loop framework to strategically deploy simulations and experiments to guide decision making for scale-up analysis. Resources are allocated over successive batches, which makes the allocation adaptive to some objective such as optimization or model training. The framework enables the characterization and management of uncertainties associated with predictive models of complex systems when scale-up questions lead to significant model extrapolation. A key advancement of this framework is its integration of multi-fidelity surrogate modeling, uncertainty management, and automated orchestration of various computing and experimentation resources into a single integrated software package. This enables efficient multi-fidelity modeling across multiple computing resources by incorporating real-world constraints such as relative queue times and throughput on individual machines into the multi-fidelity sampling decision. We discuss applications of this framework to problems in the renewable energy space, including biofuels production, material synthesis, perovskite crystal growth, and building electrical loads.

math.OC↗

arXiv2025

Bayesian calibration of bubble size dynamics applied to CO2 gas fermenters

To accelerate the scale-up of gaseous CO2 fermentation reactors, computational models need to predict gas-to-liquid mass transfer which requires capturing the bubble size dynamics, i.e. bubble breakup and coalescence. However, the applicability of existing models beyond air-water mixtures remains to be established. Here, an inverse modeling approach, accelerated with a neural network surrogate, calibrates the breakup and coalescence closure models, that are used in class methods for population balance modeling (PBM). The calibration is performed based on experimental results obtained in a CO2-air-water-coflowing bubble column reactor. Bayesian inference is used to account for noise in the experimental dataset and bias in the simulation results. To accurately capture gas holdup and interphase mass transfer, the results show that the breakage rate needs to be increased by one order of magnitude. The inferred model parameters are then used on a separate configuration and shown to also improve bubble size distribution predictions.

physics.flu-dyn↗

arXiv2024

Triboplasma assisted chemical conversion in granular systems: a semi-analytic model

We present a semi-analytic model for a novel plasma-assisted chemical conversion pathway using triboplasmas generated in granular flows. Triboelectric charge relaxation is a well known phenomena where the potential generated from contact charging of particles exceeds the breakdown voltage of the background gas. In this work, we extend the triboelectric charge relaxation theory to include non equilibrium plasma energy and particle balance equations to predict the formation of dissociated and excited species that act as precursors to chemical conversion, for example in plasma-assisted ammonia synthesis. Our example case study with nitrogen background gas and teflon/aluminum tribomaterial system yielded high excited nitrogen species densities per collision that are comparable to current plasma-assisted conversion pathways. We also present a regime diagram for various gases where Paschen breakdown parameters are used to determine whether triboplasmas can be formed for a given effective work-function difference between two materials. Our sensitivity studies indicate particle velocity, particle radius, solids fraction and space charge effects play a critical role in overall plasma densities and excited species production.

physics.plasm-ph↗

arXiv2021

Surface chemistry models for GaAs epitaxial growth and hydride cracking using reacting flow simulations

Hydride Vapor Phase Epitaxy (HVPE) is a promising technology that can aid in the cost reduction of III-V materials and devices manufacturing, particularly high-efficiency solar cells for space and terrestrial applications. However, recent demonstrations of ultra fast growth rates ($\sim$ 500 $μ$m/h) via uncracked hydrides are not well described by present models for the growth. Therefore, it is necessary to understand the kinetics of the growth process and its coupling with transport phenomena, so as to enable fast and uniform epitaxial growth. In this work, we derive a kinetic model using experimental data and integrate it into a computational fluid dynamics simulation of an HVPE growth reactor. We also modify an existing hydride cracking model that we validate against numerical simulations and experimental data. We show that the developed growth model and the improved cracking model are able to reproduce experimental growth measurements of \ce{GaAs} in an existing HVPE system.

physics.app-ph↗

arXiv2021

Performance comparison of CFD-DEM solver MFiX-Exa, on GPUs and CPUs

We present computational performance comparisons of gas-solid simulations performed on current CPU and GPU architectures using MFiX Exa, a CFD-DEM solver that leverages hybrid CPU+GPU parallelism. A representative fluidized bed simulation with varying particle numbers from 2 to 67 million is used to compare serial and parallel performance. A single GPU was observed to be about 10 times faster compared to a single CPU core. The use of 3 GPUs on a single compute node was observed to be 4x faster than using all 64 CPU cores. We also observed that using an error controlled adaptive time stepping scheme for particle advance provided a consistent 4x speed-up on both CPUs and GPUs. Weak scaling results indicate superior parallel efficiencies when using GPUs compared to CPUs for the problem sizes studied in this work.

cs.DC↗

arXiv2018

An adaptive timestepping methodology for particle advance in coupled CFD-DEM simulations

An adpative integration technique for time advancement of particle motion in the context of coupled computational fluid dynamics (CFD) - discrete element method (DEM) simulations is presented in this work. CFD-DEM models provide an accurate description of multiphase physical systems where a granular phase exists in an underlying continuous medium. The time integration of the granular phase in these simulations present unique computational challenges due to large variations in time scales associated with particle collisions. The algorithm presented in this work uses a local time stepping approach to resolve collisional time scales for only a subset of particles that are in close proximity to potential collision partners, thereby resulting in substantial reduction of computational cost. This approach is observed to be 2-3X faster than traditional explicit methods for problems that involve both dense and dilute regions, while maintaining the same level of accuracy.

physics.comp-ph↗